Introduction

Crime impacts communities.

This analysis explores the data collected by the Los Angeles County Sheriff’s Department (LACSD) from the years 2004 to 2015. This data was exported from LACSD’s RMS (Records Management System). The data analyzed is located at https://data.lacounty.gov/Criminal/LA-SHERIFF-CRIMES-FROM-2004-TO-2015/3dxh-c6jw

Synopsis

The following are the findings from the analysis:

Data Processing

Load packages that are needed for the processing of the data, generation of the graphical figures and analysis of the data.

library(plyr)
library(dplyr)
library(ggplot2)
library(grid)
library(gridExtra)
library(ggmap)

Define functions that are needed.

Download and read the LACSD Crime Data file into memory.

sourceUrl <- "https://data.lacounty.gov/api/views/3dxh-c6jw/rows.csv?accessType=DOWNLOAD"
targetFile <- "LA_SHERIFF_CRIMES_FROM_2004_TO_2015.csv"

# if we haven't already downloaded the file, download it
if (!file.exists(targetFile))
    download.file(sourceUrl, destfile = targetFile)

# read the file into memory making empty fields NA
crimeData <- read.csv(targetFile, na.strings=c("", "NA"))

Quickly review the data

summary(crimeData)
##                   CRIME_DATE        CRIME_YEAR   CRIME_CATEGORY_NUMBER
##  07/02/2006 12:07:00 AM:    200   Min.   :2004   Min.   : 1.00        
##  07/02/2007 12:07:00 AM:    171   1st Qu.:2006   1st Qu.: 6.00        
##  07/01/2008 12:07:00 PM:    165   Median :2009   Median :13.00        
##  07/02/2005 12:07:00 AM:    164   Mean   :2009   Mean   :13.61        
##  12/27/2006 03:12:51 PM:    160   3rd Qu.:2012   3rd Qu.:23.00        
##  01/01/2004 12:01:00 AM:    152   Max.   :2015   Max.   :30.00        
##  (Other)               :2332637                                       
##           CRIME_CATEGORY_DESCRIPTION STATISTICAL_CODE
##  LARCENY THEFT         :407492       Min.   : 11.0   
##  VEHICLE / BOATING LAWS:350628       1st Qu.: 92.0   
##  NARCOTICS             :268700       Median :184.0   
##  BURGLARY              :177989       Mean   :197.3   
##  VANDALISM             :172087       3rd Qu.:261.0   
##  GRAND THEFT AUTO      :170242       Max.   :695.0   
##  (Other)               :786511                       
##                                       STATISTICAL_CODE_DESCRIPTION
##  VEHICLE AND BOATING LAWS: Misdemeanor              : 276216      
##  GRAND THEFT VEHICLE (GTA): Automobile/Passenger Van: 137433      
##  VEHICLE BURGLARY: Auto/Passenger Van Burglary      : 105926      
##  VANDALISM MISD                                     :  78628      
##  ASSAULT, NON-AGG: Hands, Feet, Fist, Etc.          :  75914      
##  NARCOTICS: Marijuana Misdemeanors (Less Than 1 oz) :  71139      
##  (Other)                                            :1588393      
##   VICTIM_COUNT                            STREET       
##  Min.   : 1.000   450 BAUCHET ST             :  13875  
##  1st Qu.: 1.000   29340 THE OLD ROAD         :   8056  
##  Median : 1.000   440 BAUCHET ST             :   6879  
##  Mean   : 1.027   1000 UNIVERSAL STUDIOS BLVD:   3285  
##  3rd Qu.: 1.000   20700 S AVALON BLVD        :   3189  
##  Max.   :53.000   (Other)                    :2265848  
##                   NA's                       :  32517  
##           CITY             STATE              ZIP         
##  LOS ANGELES: 267601   CA     :2310697   Min.   :    9    
##  LANCASTER  : 189984   NV     :    135   1st Qu.:90260    
##  COMPTON    : 141579   TX     :     95   Median :90706    
##  PALMDALE   : 137426   NY     :     86   Mean   :90971    
##  CARSON     :  86702   FL     :     76   3rd Qu.:91384    
##  (Other)    :1488451   (Other):    541   Max.   :98807    
##  NA's       :  21906   NA's   :  22019   NA's   :1088737  
##     LATITUDE            LONGITUDE      GANG_RELATED REPORTING_DISTRICT
##  Min.   :-148729882   Min.   :-274.5   N:2243404    2610   :  24976   
##  1st Qu.:        34   1st Qu.:-118.3   Y:  90245    2608   :  22644   
##  Median :        34   Median :-118.2                2607   :  20919   
##  Mean   :    -15704   Mean   :-118.2                2611   :  18630   
##  3rd Qu.:        34   3rd Qu.:-118.1                1137   :  18450   
##  Max.   :        47   Max.   :  37.8                1335   :  18091   
##  NA's   :139917       NA's   :139917                (Other):2209939   
##  STATION_IDENTIFIER     STATION_NAME     CRIME_IDENTIFIER  
##  CA0190013: 206937   LAKEWOOD : 206937   Min.   :12354772  
##  CA0190024: 198451   LANCASTER: 198451   1st Qu.:13721760  
##  CA01900V3: 176148   CENTURY  : 176148   Median :14793037  
##  CA01900W9: 152258   PALMDALE : 152258   Mean   :14868051  
##  CA0190004: 146373   NORWALK  : 146373   3rd Qu.:16075134  
##  CA0190042: 141182   COMPTON  : 141182   Max.   :17659931  
##  (Other)  :1312300   (Other)  :1312300                     
##                                             GEO_CRIME_LOCATION 
##  (34.05913144836631699721, -118.23115765035327852035):   7489  
##  (34.05914118481381540764, -118.23162950988520228384):   5802  
##  (34.45160990721858960867, -118.61520280236499624484):   4042  
##  (34.05914139622937544171, -118.23106492756480129503):   3478  
##  (34.05926873298660148271, -118.23148712692719813448):   2501  
##  (Other)                                             :2179297  
##  NA's                                                : 131040

Tidy the data. There are several issues with the data. Zip codes are missing in several row. Latitude and Longitude are also missing in several rows. Several Latitude and Longitude points are outside the LAC area, with points as far north as Bakersfield, and as far south as San Diego. Since the final output will use Lat/Long, rows with missing values will be removed as well as the points outside the LAC boundaries. The zip code column will be removed entirely.

# remove columns not needed
crimeData <- subset(crimeData, select= -c(ZIP,GEO_CRIME_LOCATION))

# remove rows where NA is in LAT or Long
crimeData <- crimeData[complete.cases(crimeData[,c("LATITUDE","LONGITUDE")]),] 

# remove rows where LAT and/or Long are outside the LAC area
crimeData <- crimeData[crimeData$LATITUDE <= 34.337306,]
crimeData <- crimeData[crimeData$LATITUDE >= 33.703652,]
crimeData <- crimeData[crimeData$LONGITUDE >= -118.668176,]
crimeData <- crimeData[crimeData$LONGITUDE <= -118.155289,]

summary(crimeData)
##                   CRIME_DATE       CRIME_YEAR   CRIME_CATEGORY_NUMBER
##  02/24/2009 10:02:00 AM:   108   Min.   :2004   Min.   : 1.00        
##  07/01/2008 08:07:00 AM:    68   1st Qu.:2006   1st Qu.: 6.00        
##  05/24/2006 04:05:10 PM:    52   Median :2009   Median :13.00        
##  07/01/2008 12:07:00 PM:    52   Mean   :2009   Mean   :13.42        
##  01/01/2007 12:01:00 AM:    48   3rd Qu.:2012   3rd Qu.:23.00        
##  01/23/2008 09:01:00 AM:    48   Max.   :2015   Max.   :30.00        
##  (Other)               :877343                                       
##            CRIME_CATEGORY_DESCRIPTION STATISTICAL_CODE
##  LARCENY THEFT          :130820       Min.   : 11.0   
##  VEHICLE / BOATING LAWS :122264       1st Qu.: 91.0   
##  NARCOTICS              :116875       Median :182.0   
##  GRAND THEFT AUTO       : 71471       Mean   :188.8   
##  NON-AGGRAVATED ASSAULTS: 66890       3rd Qu.:255.0   
##  BURGLARY               : 59556       Max.   :695.0   
##  (Other)                :309843                       
##                                                        STATISTICAL_CODE_DESCRIPTION
##  VEHICLE AND BOATING LAWS: Misdemeanor                               : 99211       
##  GRAND THEFT VEHICLE (GTA): Automobile/Passenger Van                 : 61632       
##  VEHICLE BURGLARY: Auto/Passenger Van Burglary                       : 37201       
##  ASSAULT, NON-AGG: Hands, Feet, Fist, Etc.                           : 36700       
##  Felony Transport. &/or Sale of Controlled Substance(excpt Marijuana): 32108       
##  Felony Possession of a Controlled Substance (excluding Marijuana)   : 26752       
##  (Other)                                                             :584115       
##   VICTIM_COUNT                            STREET      
##  Min.   : 1.000   450 BAUCHET ST             : 13833  
##  1st Qu.: 1.000   440 BAUCHET ST             :  6868  
##  Median : 1.000   1000 UNIVERSAL STUDIOS BLVD:  3220  
##  Mean   : 1.033   20700 S AVALON BLVD        :  3186  
##  3rd Qu.: 1.000   11710 S ALAMEDA ST         :  2698  
##  Max.   :53.000   7100 SANTA MONICA BLVD     :  2288  
##                   (Other)                    :845626  
##              CITY            STATE           LATITUDE       LONGITUDE     
##  LOS ANGELES   :245297   CA     :877676   Min.   :33.71   Min.   :-118.7  
##  COMPTON       :136451   AZ     :     2   1st Qu.:33.89   1st Qu.:-118.3  
##  CARSON        : 83428   MN     :     2   Median :33.93   Median :-118.2  
##  LYNWOOD       : 65795   CO     :     1   Mean   :33.95   Mean   :-118.3  
##  WEST HOLLYWOOD: 59434   FL     :     1   3rd Qu.:34.02   3rd Qu.:-118.2  
##  PARAMOUNT     : 37124   (Other):     7   Max.   :34.34   Max.   :-118.2  
##  (Other)       :250190   NA's   :    30                                   
##  GANG_RELATED REPORTING_DISTRICT STATION_IDENTIFIER
##  N:833488     2112   : 12962     CA01900V3:168899  
##  Y: 44231     5100   : 11649     CA0190042:133980  
##               1624   : 10820     CA0190016:104087  
##               2116   : 10818     CA0190003: 86143  
##               0972   : 10555     CA0190002: 73861  
##               0977   : 10283     CA0190009: 66352  
##               (Other):810632     (Other)  :244397  
##             STATION_NAME    CRIME_IDENTIFIER  
##  CENTURY          :168899   Min.   :12354773  
##  COMPTON          :133980   1st Qu.:13788514  
##  CARSON           :104087   Median :14871754  
##  SOUTH LOS ANGELES: 86143   Mean   :14920747  
##  EAST LOS ANGELES : 73861   3rd Qu.:16127086  
##  WEST HOLLYWOOD   : 66352   Max.   :17659931  
##  (Other)          :244397

A simple plot of all the points in LAC.

#Using GGPLOT, plot the City Map for all years
lac <- get_map(location=c(lon=-118.411732, lat=34.020479), zoom="auto",  maptype="roadmap") 
## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=34.020479,-118.411732&zoom=10&size=640x640&scale=2&maptype=roadmap&language=en-EN&sensor=false
map = ggmap(lac)

mapPoints <- map + geom_point(data=crimeData, aes(x=LONGITUDE, y=LATITUDE, colour=factor(CRIME_CATEGORY_DESCRIPTION)), alpha=.5, size=1) + theme(legend.position = "none")

mapPoints

We can make the Shiny map show crimes for the years from 2004 to 2015.

crime2004 <- crimeData[crimeData$CRIME_YEAR == 2004,]
crime2005 <- crimeData[crimeData$CRIME_YEAR == 2005,]
crime2006 <- crimeData[crimeData$CRIME_YEAR == 2006,]
crime2007 <- crimeData[crimeData$CRIME_YEAR == 2007,]
crime2008 <- crimeData[crimeData$CRIME_YEAR == 2008,]
crime2009 <- crimeData[crimeData$CRIME_YEAR == 2009,]
crime2010 <- crimeData[crimeData$CRIME_YEAR == 2010,]
crime2011 <- crimeData[crimeData$CRIME_YEAR == 2011,]
crime2012 <- crimeData[crimeData$CRIME_YEAR == 2012,]
crime2013 <- crimeData[crimeData$CRIME_YEAR == 2013,]
crime2014 <- crimeData[crimeData$CRIME_YEAR == 2014,]
crime2015 <- crimeData[crimeData$CRIME_YEAR == 2015,]

summary(crime2004)
##                   CRIME_DATE      CRIME_YEAR   CRIME_CATEGORY_NUMBER
##  01/01/2004 12:01:00 AM:   47   Min.   :2004   Min.   : 1.00        
##  02/09/2004 08:02:00 AM:   38   1st Qu.:2004   1st Qu.: 6.00        
##  07/01/2004 12:07:00 PM:   34   Median :2004   Median :13.00        
##  04/14/2004 01:04:00 PM:   33   Mean   :2004   Mean   :13.19        
##  01/26/2004 08:01:00 AM:   30   3rd Qu.:2004   3rd Qu.:23.00        
##  07/02/2004 12:07:00 AM:   28   Max.   :2004   Max.   :30.00        
##  (Other)               :72480                                       
##            CRIME_CATEGORY_DESCRIPTION STATISTICAL_CODE
##  LARCENY THEFT          :10736        Min.   : 11.0   
##  VEHICLE / BOATING LAWS :10232        1st Qu.: 91.0   
##  NARCOTICS              : 7576        Median :181.0   
##  GRAND THEFT AUTO       : 7327        Mean   :186.6   
##  NON-AGGRAVATED ASSAULTS: 5306        3rd Qu.:255.0   
##  BURGLARY               : 5119        Max.   :612.0   
##  (Other)                :26394                        
##                                                        STATISTICAL_CODE_DESCRIPTION
##  VEHICLE AND BOATING LAWS: Misdemeanor                               : 8503        
##  GRAND THEFT VEHICLE (GTA): Automobile/Passenger Van                 : 6403        
##  VEHICLE BURGLARY: Auto/Passenger Van Burglary                       : 3723        
##  ASSAULT, NON-AGG: Hands, Feet, Fist, Etc.                           : 2996        
##  VANDALISM MISD                                                      : 2680        
##  Felony Transport. &/or Sale of Controlled Substance(excpt Marijuana): 2513        
##  (Other)                                                             :45872        
##   VICTIM_COUNT                            STREET     
##  Min.   : 1.000   450 BAUCHET ST             : 1069  
##  1st Qu.: 1.000   440 BAUCHET ST             :  812  
##  Median : 1.000   1000 UNIVERSAL STUDIOS BLVD:  427  
##  Mean   : 1.036   710 S LONG BEACH BLVD      :  228  
##  3rd Qu.: 1.000   20700 S AVALON BLVD        :  214  
##  Max.   :19.000   100 UNIVERSAL CITY PLZ     :  200  
##                   (Other)                    :69740  
##                CITY           STATE          LATITUDE       LONGITUDE     
##  LOS ANGELES     :17470   CA     :72688   Min.   :33.72   Min.   :-118.7  
##  COMPTON         :11645   PA     :    1   1st Qu.:33.89   1st Qu.:-118.3  
##  CARSON          : 7510   AK     :    0   Median :33.92   Median :-118.2  
##  LYNWOOD         : 5234   AL     :    0   Mean   :33.95   Mean   :-118.3  
##  WEST HOLLYWOOD  : 4691   AR     :    0   3rd Qu.:34.02   3rd Qu.:-118.2  
##  EAST LOS ANGELES: 4058   (Other):    0   Max.   :34.33   Max.   :-118.2  
##  (Other)         :22082   NA's   :    1                                   
##  GANG_RELATED REPORTING_DISTRICT STATION_IDENTIFIER
##  N:67996      2112   : 1294      CA01900V3:13633   
##  Y: 4694      5100   : 1230      CA0190042:11499   
##               1624   :  911      CA0190016: 9331   
##               2170   :  894      CA0190003: 6644   
##               0977   :  863      CA0190009: 5877   
##               2846   :  856      CA0190002: 5206   
##               (Other):66642      (Other)  :20500   
##             STATION_NAME   CRIME_IDENTIFIER  
##  CENTURY          :13633   Min.   :12354773  
##  COMPTON          :11499   1st Qu.:12471805  
##  CARSON           : 9331   Median :12697530  
##  SOUTH LOS ANGELES: 6644   Mean   :12680362  
##  WEST HOLLYWOOD   : 5877   3rd Qu.:12824914  
##  EAST LOS ANGELES : 5206   Max.   :17069979  
##  (Other)          :20500
summary(crime2015)
##                   CRIME_DATE      CRIME_YEAR   CRIME_CATEGORY_NUMBER
##  04/15/2015 12:04:00 PM:   24   Min.   :2015   Min.   : 1.00        
##  02/26/2015 12:02:00 PM:   21   1st Qu.:2015   1st Qu.: 6.00        
##  04/08/2015 12:04:00 PM:   21   Median :2015   Median :13.00        
##  10/26/2015 03:10:00 PM:   21   Mean   :2015   Mean   :12.92        
##  02/13/2015 08:02:00 PM:   20   3rd Qu.:2015   3rd Qu.:22.00        
##  03/18/2015 01:03:00 PM:   20   Max.   :2015   Max.   :30.00        
##  (Other)               :62807                                       
##            CRIME_CATEGORY_DESCRIPTION STATISTICAL_CODE
##  LARCENY THEFT          :11326        Min.   : 11.0   
##  VEHICLE / BOATING LAWS : 8236        1st Qu.: 91.0   
##  NON-AGGRAVATED ASSAULTS: 6702        Median :183.0   
##  NARCOTICS              : 5484        Mean   :193.3   
##  GRAND THEFT AUTO       : 5316        3rd Qu.:261.0   
##  VANDALISM              : 4473        Max.   :695.0   
##  (Other)                :21397                        
##                                                        STATISTICAL_CODE_DESCRIPTION
##  VEHICLE AND BOATING LAWS: Misdemeanor                               : 5975        
##  GRAND THEFT VEHICLE (GTA): Automobile/Passenger Van                 : 4511        
##  ASSAULT, NON-AGG: Hands, Feet, Fist, Etc.                           : 3936        
##  VEHICLE BURGLARY: Auto/Passenger Van Burglary                       : 2771        
##  Misdemeanor Possessn of a Controlled Substance (excluding Marijuana): 2715        
##  ASSAULT, NON-AGGRAVATED: DOMESTIC VIOLENCE                          : 2116        
##  (Other)                                                             :40910        
##   VICTIM_COUNT                           STREET     
##  Min.   :1.000   450 BAUCHET ST             : 1849  
##  1st Qu.:1.000   11710 S ALAMEDA ST         :  442  
##  Median :1.000   7100 SANTA MONICA BLVD     :  257  
##  Mean   :1.039   440 BAUCHET ST             :  218  
##  3rd Qu.:1.000   20700 S AVALON BLVD        :  216  
##  Max.   :9.000   1000 UNIVERSAL STUDIOS BLVD:  201  
##                  (Other)                    :59751  
##              CITY           STATE          LATITUDE       LONGITUDE     
##  LOS ANGELES   :21367   CA     :62932   Min.   :33.72   Min.   :-118.7  
##  COMPTON       : 8126   FL     :    1   1st Qu.:33.89   1st Qu.:-118.3  
##  CARSON        : 5479   AK     :    0   Median :33.93   Median :-118.2  
##  LYNWOOD       : 4267   AL     :    0   Mean   :33.96   Mean   :-118.3  
##  WEST HOLLYWOOD: 3859   AR     :    0   3rd Qu.:34.03   3rd Qu.:-118.2  
##  PARAMOUNT     : 2579   (Other):    0   Max.   :34.34   Max.   :-118.2  
##  (Other)       :17257   NA's   :    1                                   
##  GANG_RELATED REPORTING_DISTRICT STATION_IDENTIFIER
##  N:61336      5100   : 1276      CA01900V3:10449   
##  Y: 1598      5800   : 1199      CA0190042: 7829   
##               2112   :  778      CA0190002: 7112   
##               0972   :  724      CA0190016: 7033   
##               0977   :  720      CA0190003: 6276   
##               1624   :  673      CA0190009: 4228   
##               (Other):57564      (Other)  :20007   
##             STATION_NAME   CRIME_IDENTIFIER  
##  CENTURY          :10449   Min.   :14853777  
##  COMPTON          : 7829   1st Qu.:17067434  
##  EAST LOS ANGELES : 7112   Median :17154730  
##  CARSON           : 7033   Mean   :17238295  
##  SOUTH LOS ANGELES: 6276   3rd Qu.:17461241  
##  WEST HOLLYWOOD   : 4228   Max.   :17659931  
##  (Other)          :20007

The summary shows that overall crime has gone down from 2004 to 2015. Great Job LAC!

Plots to compare between 2004 and 2015.

#Using GGPLOT, plot the City Map for 2004 only
mapPoints2004 <- map + geom_point(data=crime2004, aes(x=LONGITUDE, y=LATITUDE, colour=factor(CRIME_CATEGORY_DESCRIPTION)), alpha=.5, size=1) + theme(legend.position = "none")

mapPoints2004

mapPoints2005 <- map + geom_point(data=crime2005, aes(x=LONGITUDE, y=LATITUDE, colour=factor(CRIME_CATEGORY_DESCRIPTION)), alpha=.5, size=1) + theme(legend.position = "none")

mapPoints2005

mapPoints2006 <- map + geom_point(data=crime2006, aes(x=LONGITUDE, y=LATITUDE, colour=factor(CRIME_CATEGORY_DESCRIPTION)), alpha=.5, size=1) + theme(legend.position = "none")

mapPoints2006

mapPoints2007 <- map + geom_point(data=crime2007, aes(x=LONGITUDE, y=LATITUDE, colour=factor(CRIME_CATEGORY_DESCRIPTION)), alpha=.5, size=1) + theme(legend.position = "none")

mapPoints2007

mapPoints2008 <- map + geom_point(data=crime2008, aes(x=LONGITUDE, y=LATITUDE, colour=factor(CRIME_CATEGORY_DESCRIPTION)), alpha=.5, size=1) + theme(legend.position = "none")

mapPoints2008

mapPoints2009 <- map + geom_point(data=crime2009, aes(x=LONGITUDE, y=LATITUDE, colour=factor(CRIME_CATEGORY_DESCRIPTION)), alpha=.5, size=1) + theme(legend.position = "none")

mapPoints2009

#Using GGPLOT, plot the City Map for 2015 only
mapPoints2010 <- map + geom_point(data=crime2010, aes(x=LONGITUDE, y=LATITUDE, colour=factor(CRIME_CATEGORY_DESCRIPTION)), alpha=.5, size=1) + theme(legend.position = "none")

mapPoints2010

mapPoints2011 <- map + geom_point(data=crime2011, aes(x=LONGITUDE, y=LATITUDE, colour=factor(CRIME_CATEGORY_DESCRIPTION)), alpha=.5, size=1) + theme(legend.position = "none")

mapPoints2011

mapPoints2012 <- map + geom_point(data=crime2012, aes(x=LONGITUDE, y=LATITUDE, colour=factor(CRIME_CATEGORY_DESCRIPTION)), alpha=.5, size=1) + theme(legend.position = "none")

mapPoints2012

mapPoints2013 <- map + geom_point(data=crime2013, aes(x=LONGITUDE, y=LATITUDE, colour=factor(CRIME_CATEGORY_DESCRIPTION)), alpha=.5, size=1) + theme(legend.position = "none")

mapPoints2013

mapPoints2014 <- map + geom_point(data=crime2014, aes(x=LONGITUDE, y=LATITUDE, colour=factor(CRIME_CATEGORY_DESCRIPTION)), alpha=.5, size=1) + theme(legend.position = "none")

mapPoints2014

mapPoints2015 <- map + geom_point(data=crime2015, aes(x=LONGITUDE, y=LATITUDE, colour=factor(CRIME_CATEGORY_DESCRIPTION)), alpha=.5, size=1) + theme(legend.position = "none")

mapPoints2015

There is a strange concentration of color, zooming in we find that the crime type is at LAX airport.

#Using GGPLOT, plot the City Map for all years
lac <- get_map(location=c(lon=-118.411732, lat=33.950479), zoom=14,  maptype="roadmap") 
## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=33.950479,-118.411732&zoom=14&size=640x640&scale=2&maptype=roadmap&language=en-EN&sensor=false
map = ggmap(lac)

mapPoints <- map + geom_point(data=crimeData, aes(x=LONGITUDE, y=LATITUDE, colour=factor(CRIME_CATEGORY_DESCRIPTION)), alpha=.5, size=1) + theme(legend.position = "none")

mapPoints
## Warning: Removed 874789 rows containing missing values (geom_point).

saveToFile <- "CrimeData.csv"
saveToFileSmall <- "CrimeDataSmall.csv"

if (!file.exists(saveToFile))
  write.table(crimeData, saveToFile, sep=",")

if (!file.exists(saveToFileSmall))
{
  smallCrime <- crimeData[crimeData$CRIME_YEAR >= 2011,]
  write.table(smallCrime, saveToFileSmall, sep=",")
}